The figure plots the mean (bar) and 95% CI (whisker) for the fraction of ELISA-RBD tests (A and B) and RT-PCR tests (C and D) that yielded positive results (y-axis) at each location, defined as the urban or rural portion of the labeled region. Test results for rural and urban locations are shown on separate plots.
eAppendix. Sample Frame and Location, Data Collection, and Statistical Methods
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Mohanan M, Malani A, Krishnan K, Acharya A. Prevalence of SARS-CoV-2 in Karnataka, India. JAMA. 2021;325(10):1001–1003. doi:10.1001/jama.2021.0332
Low- and middle-income countries contain the majority of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). India has the second highest number of reported cases, but most seroprevalence estimates come from cities. Cities, with denser population, are more vulnerable to SARS-CoV-2. However, millions of city workers fled to rural India where lockdown was less stringent.
We assessed SARS-CoV-2 prevalence among volunteers from population-representative households in urban and rural areas of the state of Karnataka (population, 67.5 million).
This study was approved by the Indian government and institutional review boards at participating institutions. Written informed consent was obtained from all participants (orally if respondents were unable to read or write). The sample was drawn from a population-representative panel survey, the Consumer Pyramids Household Survey1 (CPHS) (details in the eAppendix in the Supplement). The primary sampling units were towns (urban) or villages (rural); the ultimate sampling units were households. From the CPHS’s 9717 Karnataka households, we randomly selected 2912 to represent urban and rural areas of 5 state regions. We surveyed consenting household members aged 12 years or older between June 15 and August 29, 2020 (during partial lockdown). We requested 5 mL of blood and a nasopharyngeal swab from 1 volunteer per household. We compared the sex and age distribution of volunteers with the CPHS and the 2021 projection from the 2011 census.
We tested for IgG antibodies to SARS-CoV-2 receptor binding domain using an enzyme-linked immunosorbent assay with 84.7% sensitivity and 100% specificity.2 The test result is positive when the ratio of IgG titer in a sample to a negative control exceeds 1.5. We conducted reverse transcriptase–polymerase chain reaction tests targeting the N gene using the R-Gene assay (BioMérieux). Cycle threshold values less than 34 indicate a positive test result. The test has 100% sensitivity and specificity.3
We estimated the adjusted proportions of positive test results in locations (defined by regions and urban status) using weights to account for sampling probabilities and random nonresponse. Inadequate samples were treated as missing in the analysis. When aggregating across locations, we reweighted adjusted proportions by the location’s population. We estimated 95% CIs using bootstrap methods with 1000 replications per location. We calculated adjusted seroprevalence from adjusted proportions using the Rogan-Gladen4 formula to correct for test inaccuracy (eAppendix in the Supplement). Analyses were conducted in Stata version 16.1 (StataCorp).
We received survey consent from members of 1907 households (65.5% of 2912 sampled households), blood samples from 1386 persons (47.6% of sampled households; 72.7% of surveyed households) and swabs from 1397 persons (48.0% of sampled households; 73.3% of surveyed households), and results from 1197 blood samples and 1341 swabs. The primary reasons for missing results were insufficient blood and bad viral transport medium. Persons aged 40 to 59 years were overrepresented among participants contributing specimens relative to the census (Table).
The adjusted proportion of positive IgG test results ranged from 22.8% to 53.1% across rural and 30.9% to 76.8% across urban areas (Figure). Overall rural-, urban-, and statewide-adjusted proportions were 37.4% (95% CI, 32.9%-41.8%), 45.6% (95% CI, 38.1%-53.1%), and 39.6% (95% CI, 35.7%-43.4%), respectively.
Rural, urban, and statewide seroprevalences adjusted for test sensitivity were 44.1% (95% CI, 40.0%-48.2%), 53.8% (95% CI, 48.4%-59.2%), and 46.7% (95% CI, 43.3%-50.0%), respectively.
The adjusted proportion of positive polymerase chain reaction test results ranged from 1.5% to 7.7% across rural areas and 4.0% to 10.5% across urban areas (Figure). Overall rural-, urban-, and statewide-adjusted proportions were 3.6% (95% CI, 2.2%-4.9%), 6.8% (95% CI, 3.5%-10.1%), and 4.3% (95% CI, 3.1%-5.7%), respectively.
The adjusted seroprevalence of SARS-CoV-2 across Karnataka was 46.7%, suggesting approximately 31.5 million residents were infected, far greater than the 327 076 cases reported by August 29, 2020.5 This discrepancy may be due to low testing rates (approximately 4000 per 1 million population)5 and a large proportion of infections in Karnataka being asymptomatic.6
The main study limitation is that testing volunteers may produce selection bias because volunteers may not be representative of the population. Because asymptomatic people may not know their disease status, their status may not influence participation. Moreover, fear of quarantine may discourage participation, while free testing to determine immunity may encourage participation.
The findings have implications for infection containment measures.
Accepted for Publication: January 11, 2021.
Published Online: February 4, 2021. doi:10.1001/jama.2021.0332
Corresponding Author: Anup Malani, PhD, University of Chicago, 1111 E 60th St, Chicago, IL 60622 (email@example.com).
Author Contributions: Drs Mohanan and Malani had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Mohanan, Malani.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Mohanan, Malani.
Obtained funding: Mohanan, Acharya.
Administrative, technical, or material support: Mohanan, Krishnan, Acharya.
Supervision: Mohanan, Acharya.
Conflict of Interest Disclosures: Dr Malani reported receiving an award from Emergent Ventures used to fund research managers and assistants across a range of coronavirus disease 2019–related projects. Dr Krishnan reported that the Centre for Monitoring Indian Economy was paid to execute the survey from which data were used for this study. No other disclosures were reported.
Funding/Support: This study was funded by an Action Covid-19 Team grant awarded to IDFC Foundation (Mumbai, India).
Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: We are grateful to the government of India (K. Vijay Raghavan, PhD, principal scientific advisor) and the government of Karnataka (Jawaid Akhtar, IAS, Pankaj Pandey, IAS, Selva Kumar, PhD, IAS, Gunjan Krishna, IAS, Karnataka Covid-19 Technical Advisory Committee, Manoj Kolla, Prakash Kumar, MD) for supporting and enabling this effort. We thank Gagandeep Kang, MD, PhD (Translational Health Science and Technology Institute), Vasanthapuram Ravi, PhD (National Institute of Mental Health and Neuro-Sciences), and Mahesh Vyas (Center for Monitoring Indian Economy) for timely advice and facilitating the project. We thank Reuben Abraham, Pritika Hingorani, and the IDFC Foundation team for supporting all aspects of the project. None of the individuals listed received any compensation for support or contributions to the study. We are thankful to Lipika Kapoor and Saloni Taneja for project management and to all team members from the Centre for Monitoring Indian Economy and the project team for undertaking challenging fieldwork during the epidemic. All diagnostic testing in the project was conducted at Aster Labs and Xcyton Labs.
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